ArticlePeerJ2020
Convolutional neural network in upper limb functional motion analysis after stroke.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Performance Insights in Speed Climbing: Quantitative and Qualitative Analysis of Key Movement Metrics.Bioengineering (Basel, Switzerland) · 2025Article
- Enhancing rehabilitation in stroke survivors: a deep learning approach to access upper extremity movement using accelerometry data.Frontiers in artificial intelligence · 2025Article
- Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN.Sensors (Basel, Switzerland) · 2024Article
- Quantitative Assessment of Upper Limb Movement in Post-Stroke Adults for Identification of Sensitive Measures in Reaching and Lifting Activities.Journal of clinical medicine · 2023Article
- Combining 3D skeleton data and deep convolutional neural network for balance assessment during walking.Frontiers in bioengineering and biotechnology · 2023Article
- Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals.Sensors (Basel, Switzerland) · 2021Review
- Summary of over Fifty Years with Brain-Computer Interfaces-A Review.Brain sciences · 2021Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this work, implementation of Convolutional Neural Network (CNN) for the purpose of analysis of functional upper limb movement pattern was applied. The main aim of the study was to compare motion of selected activities of daily living of participants after stroke with the healthy ones (in similar age). The optical, marker-based motion capture system was applied for the purpose of data acquisition. There were some attempts made in order to find the existing differences in the motion pattern of the upper limb. For this purpose, the motion features of dominant and non-dominant upper limb of healthy participants were compared with motion features of paresis and non-paresis upper limbs of participants after stroke. On the basis of the newly collected data set, a new CNN application was presented to the classification of motion data in two different class label configurations. Analyzing individual segments of the upper body, it turned out that the arm was the most sensitive segment for capturing changes in the trajectory of the lifting movements of objects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.